1. Field of the Invention
The present invention relates to a living body light measurement system, and, more particularly, the present invention is directed to a method for displaying images indicating a special distribution of changes in concentration and a method for displaying such images in a living body light measurement system capable of measuring the concentration of metabolic substances of a living body or the changes in the concentration using the light of the living body.
2. Description of the Background
Diagnosis of diseases in the brain can be realized by measuring, when possible, the activation of the human brain. Moreover, recovery processes and the monitoring of rehabilitation of these diagnosed brain diseases may also be realized. Therefore, various measuring systems for brain functions have been proposed.
In recent years, a practical brain activation measuring system introducing the near infrared spectral method has been proposed. Near infrared rays have a higher transmitting property for living body tissues compared to other light beams in various wavelengths (colors). Therefore, changes in blood volume at the cortex existing at the internal side of the skull can be measured. In addition, it is possible to obtain dynamic images of the changes in blood volume resulting from activation of the brain by measuring such changes at multiple locations (i.e., at multiple points). A summary of such multiple-channel light brain function measuring system has been described in Atsushi Maki, et al., “Spatial and temporal analysis of human motor activity”, Medical Physics, Vol. 22 (No. 12), pp. 1997–2005 (1995) (hereafter, “Non-Patent Document 1”). The measuring technology published by this document will be described below.
FIG. 2 illustrates the structure of a system to perform the measurement disclosed in Non-Patent Document 1 or a similar process. A subject (2-1) wears a helmet (i.e., probe) (2-2) before beginning the measurement. The probe 2-2 is alternately provided with a light illumination location in which optical fibers (S11 to S18) connected to a light source are placed and a light detection location in which optical fibers (D11 to D18) connected to a light detector are placed with interval of about 30 mm. The optical fibers connected to the light source (S11 to S18) are respectively connected to double-wavelength lasers (2-3-1 to 2-3-8 and 2-4-1 to 2-4-8) in different wavelengths. In Non-Patent Document 1, the light sources used are about 780 nm and 830 nm, around the wavelength of 800 nm, wherein the molecule extinction coefficients of oxygenation hemoglobin and de-oxygenation hemoglobin become identical.
Moreover, in FIG. 2, the light illuminated from the optical fiber S13 is detected with the optical fibers D13, D11, D15, D14 which are isolated by 30 mm from the place of the optical fiber S13. The light having reached the optical fibers for detection D11 to D18 are detected with a light detector 2-6 (semiconductor light detector, e.g., an avalanche photodiode, photomultiplier or similar device). The detected light is processed with a control and process device 2-5. A blood volume which has been changed at the cortex in accordance with the activation of the brain can be calculated on the basis of the result of processing the light at each light illumination location and each light detection location (provided alternately at intervals of 30 mm). The result of this calculation is displayed on an electronic computer including a display as waveform (time domain) and an image showing the activations (i.e., activity) of the brain.
FIG. 3 illustrates a method of measuring changes in blood volume in accordance with activations (i.e., activities) of the brain. In this figure, a propagation path (3-5) of light being propagated between a holder (3-2) for fixing an optical fiber (3-1) connected to a light source and a holder (3-4) for fixing an optical fiber (3-3) connected to a light detector. Each holder fixes an optical fiber using a screw 3-6. These holders are fixed with a resin 3-7 which also forms the helmet (2-2). As a result, the end part of the optical fiber is placed in contact with the scalp of the subject 3-8.
In FIG. 3, a typical structure of the brain of a human is also illustrated. The brain is generally formed, in addition to the scalp 3-8 described above, of the skull 3-9, cerebrospinal fluid layer 3-10, and cortex 3-11, or the like. Here, it is known that these living body tissues have known optical scattering characteristics and absorbing characteristics and that the optical scattering characteristic of the skull is particularly large.
Therefore, it is also known that the light illuminated from the light source is scattered depending on the optical scattering characteristic and is gradually lost in the intensity thereof depending on the optical absorbing characteristic. Here, the holders illustrated in the figure are located with the intervals of about 30 mm. Moreover, it is known that under this allocation interval, the light illuminated from the optical fiber 3-1 connected to the light source is propagated through the living body tissues in an arc shape 3-5 (like a banana) as illustrated and is then detected after the light has reached the optical fiber 3-3 connected to the light detector. In this figure, 3-12 illustrates the area where the blood volume increases in accordance with activation of the brain. For example, when the blood volume increases, intensity (I) of the light having reached the optical fiber for detection 3-3 is reduced.
Therefore, a change in the light absorption degree (ΔA: corresponding to a logarithmic difference value of the detected light intensity before and after activation of the brain) due to a change in the concentration of oxygenation hemoglobin and de-oxygenation hemoglobin (ΔCoxy, ΔCdeoxy) can be established as follows (equation (1)) for each wavelength λ used for measurement (λ1=780 nm and λ2=830 nm in Non-Patent Document 1):ΔA=−1n(I1/I0)=εoxyΔCoxyL+εdeoxyΔCdeoxyL  (1)
Here, L in equation (1) denotes an average optical propagation path length between the light source and the light detector. Moreover, εoxy and εdeoxy in equation (1) denote respectively the molecule extinction coefficients of oxygenation hemoglobin and de-oxygenation hemoglobin. Also in equation (1), I denotes the intensity of light arriving at the detector and I0 and I1 represent the intensity of light before activation of the brain and during activation of the brain, respectively. Changes in concentration (ΔCoxy, ΔCdeoxy) of oxygenation hemoglobin and de-oxygenation hemoglobin due to the activation of brain can be expressed as equation (2) by applying equation (1) to each wavelength:
                              (                                                                      Δ                  ⁢                                                                          ⁢                                      C                    oxy                                                                                                                        Δ                  ⁢                                                                          ⁢                                      C                    deoxy                                                                                )                =                                            (                                                                                                                  ɛ                        oxy                        λ1                                                                                                                                                ɛ                        oxy                        λ2                                                                                            ⁢                                                                                                    ɛ                        deoxy                        λ1                                                                                                                                                ɛ                        deoxy                        λ2                                                                                                        )                                      -              1                                ⁢                      (                                                                                                      -                                              ln                        ⁡                                                  (                                                                                    I                              1                                                              λ                                1                                                                                      /                                                          I                              0                                                              λ                                1                                                                                                              )                                                                                                            L                      λ1                                                                                                                                                              -                                              ln                        ⁡                                                  (                                                                                    I                              1                                                              λ                                2                                                                                      /                                                          I                              0                                                              λ                                2                                                                                                              )                                                                                                            L                      λ2                                                                                            )                                              (        2        )            
However, since it is difficult to actually determine the value of L, equation (3) may be used:
                              (                                                                      Δ                  ⁢                                                                          ⁢                                                            C                      ′                                        oxy                                                                                                                        Δ                  ⁢                                                                          ⁢                                      C                    deoxy                    ′                                                                                )                =                  L          ⁡                      (                                                                                Δ                    ⁢                                                                                  ⁢                                          C                      oxy                                                                                                                                        Δ                    ⁢                                                                                  ⁢                                          C                      deoxy                                                                                            )                                              (        3        )            
Where C′, which is a unit having the dimension wherein concentration is multiplied with distance, can be calculated as follows:
                              (                                                                      Δ                  ⁢                                                                          ⁢                                                            C                      ′                                        oxy                                                                                                                        Δ                  ⁢                                                                          ⁢                                      C                    deoxy                    ′                                                                                )                =                                            (                                                                                                                  ɛ                        oxyλ1                                                                                                                                                ɛ                        oxyλ2                                                                                            ⁢                                                                                                    ɛ                        deoxyλ1                                                                                                                                                ɛ                        deoxyλ2                                                                                                        )                                      -              1                                ⁢                      (                                                                                -                                          ln                      ⁡                                              (                                                                              I                                                          1                              ⁢                                                              λ                                1                                                                                                              /                                                      I                                                          0                              ⁢                                                              λ                                1                                                                                                                                    )                                                                                                                                                              -                                          ln                      ⁡                                              (                                                                              I                                                          1                              ⁢                                                              λ                                2                                                                                                              /                                                      I                                                          0                              ⁢                                                              λ                                2                                                                                                                                    )                                                                                                                  )                                              (        4        )            
Next, a method for imaging the result of the above calculations will be described with reference to FIG. 4 and FIG. 5. FIG. 4 illustrates a sensor locating method for the condition that eight light illumination locations for the optical fibers S11 to S18 connected to the light source and eight light detection locations for the optical fibers D11 to D18 are respectively allocated on the scalp of the subject. The eight white squares (□, 4-1) and eight black squares (▪, 4-2) indicate the light illumination points and light detection points, respectively. Moreover, the locations (4-3) indicated by the black circles are located almost at the intermediate locations between the light illumination location and the light detection location. These intermediate locations are defined as the sampling points giving the location information of change in blood volume detected from a change in the intensity of the light having reached the light detection location after illumination from the light illumination location. The reason why the sampling point has been established as an almost intermediate point between the two fibers will be described using the light propagation path 3-5 illustrated in FIG. 3. According to this light propagation path, the light is not propagated to the areas just under the light illumination locations 4-1 and light detection locations 4-2.
Moreover, at the area just under the intermediate point between the light illumination location 4-1 and light detection location 4-2, the light is propagated not only to the skull but also to the cerebrospinal fluid layer and the cortex as the brain activation area. Since the area considered as the actual brain activation area is the cortex, according to the light propagation characteristic illustrated in FIG. 4, a change in the blood volume detected by a pair of light source and light detector may be assumed to become a maximum when change in the blood volume is changed at the area just under the intermediate point of the light illumination location and the light detection location. Therefore, the intermediate point 4-3 between the light illumination location 4-1 and light detection location 4-2 is defined as the sampling point and also as the point giving the location information of change in blood volume detected by using a pair of light source and light detector. In the allocation method of the light source and light detector illustrated in FIG. 4, 24 sampling points are provided keeping the intervals of 21 mm (which is equal to √(½) times the sensor allocation interval of 30 mm).
As an example, a topographic image illustrated in FIG. 5 can be obtained by spatially interpolating the change in blood volume of the measuring area enclosed by these 24 sampling points. The areas where the change in blood volume is large can be detected by displaying a change in blood volume using contour lines and concentration lines or a similar methodology.
In FIG. 5, the areas where the change in blood volume is large are indicated as the brighter (lighter) area, while the areas where change in blood volume is small are indicated as the darker areas. The “topography” referred to in this topographic image means a “topographical map” and a space distribution of the physical amount of the dimension which is different from that of the location information on a plane is displayed on this plane. For a description of this specification, a coordinate is established in FIG. 4. This coordinate includes the x-y axes, and the origin is established at the center of the measuring areas. Therefore, x and y change in the areas of −45≦x, y≦45 and the measuring areas is extended up to 90×90 mm2.
In addition to Non-Patent Document 1 described above, some comments on the following additional references with be provided below: E. Watanabe, et al., “Noninvasive Cerebral Blood Volume Measurement During Seizures Using Multi-channel Near Infrared Spectroscopic Topography”, Journal of Biomedical Optics, 2000, July, 5(3), P. 287–290 (“Non-Patent Document 2”); E. Watanabe, et al., “Non-invasive assessment of language dominance with Near-Infrared Spectroscopic mapping”, Neurosci. Lett. 256 (1998) (“Non-Patent Document 3”); T. Yamamoto, et al., “Arranging optical fibers for the spatial resolution improvement of topographical images”, Phys. Med. Biol. 47 (2002) (“Non-Patent Document 4”); and Sandwell, David T., “Biharmonic Spline Interpolation of GEOS-3 and SEASAT Altimeter Data”, Geophysical Research Letters, 2, 139–142, 1987 (“Non-Patent Document 5”).
The topographic image illustrated in FIG. 5 is displayed under the condition that a change in blood volume is blurred. Meanwhile, such blur is rather small in the image of brain activation picked up with a functional magnetic-resonance imaging system or a positron topographic imaging system which are conventional brain function measurement systems. This is because the spatial distribution of the sampling points in the light topography method is somewhat smaller than that in the other brain function measurement systems. In the multi-channel brain function measurement system illustrated in FIG. 2, measurement is conducted by placing the optical fiber used for the measurement in contact with the scalp of the subject. At the time of generating a topographic image, the location information of a change in blood volume detected with a pair of sensors is given by establishing the intermediate point of the optical fiber location connected to the light source and detector as the sampling point.
Accordingly, unless the number of optical fibers used for measurement is increased, it is impossible to increase the spatial location density at the sampling points. However, the ability to increase the number of optical fibers is limited because the number of optical fibers gives influence on the structure of the helmet. Meanwhile, since the functional magnetic-resonance imaging system and the positron topographic imaging system are used for non-contact measurement in which the sensors are never placed in contact with the subject, the sampling points can be established in principle without any limitation. Since changes in blood volume can be measured in the multiple points, blurring of the images is somewhat small in comparison with the topographical images.
On the other hand, even when the spatial resolution is lower than that of the functional magnetic-resonance imaging system or positron topographic imaging system, a multi-channel light brain function measurement system illustrated in FIG. 2 may be used in the actual medical field. For example, the Non-Patent Documents 2 and 3 disclose that such a system is presently used for identifying the location of neurotic epilepsy and language dominance. On the basis of such documents, users estimate the activation areas from the spatial distribution of changes in blood volume displayed with the topographic images. Therefore, the accurate display of the activation areas of the brain is needed as a tool for identifying the locations of neurotic epilepsy and language dominance.
Accordingly, in order to evaluate the location accuracy of a topographic image based on the present topographic image creating algorithm and to determine the topographic image creating algorithm to obtain still higher location accuracy from the results of such an evaluation, the location accuracy of a topographical image has been evaluated using computer simulation. A simulation model is illustrated in FIG. 6. As illustrated in this figure, the structure of a human brain is modeled in a three-layer structure formed of skull 6-1, cerebrospinal fluid layer 6-2, and cortex 6-3. Such a model structure is widely used even in the documents which have already been made public. For example, this structure is described in Non-Patent Document 4. Moreover, the area 6-4 indicates a location of the brain activation area existing over the cortex.
The method for assessing the location accuracy using the model illustrated in FIG. 6 will be described with reference to FIG. 7 through FIG. 9. FIG. 7 illustrates an allocation where an optical fiber 7-1 for illumination and a fiber 7-2 for detection are placed in contact with the upper side of brain model illustrated in FIG. 6. Here, it is preferable that these two optical fibers be located with an interval of 30 mm. It is known, from Non-Patent Document 4, that the spatial distribution (sensitivity distribution) of ΔA in equation (1) when the brain activation area is changed and the light absorbing coefficient at the brain activation area is also changed for the locations of these optical fibers in locations of these optical fibers, shows the elliptical shape distribution as illustrated at the lower portion of FIG. 8. Here, the location of an optical fiber for illumination 8-1 and an optical fiber for detection 8-2 are designated.
For a qualitative expression of this elliptical shape, the spatial distribution of ΔA is expressed using the function of equation (5) given below. Here, Δx, Δy indicates the half-value widths in the directions of the x axis and y axis. Typically, Δx is known to take a value in the range of 20 to 40 mm, while Δy is known to take a value in the range of 10 to 30 mm.
                              Δ          ⁢                                          ⁢                      A            ⁡                          (                              x                ,                y                            )                                      =                              exp                          -                                                x                  2                                                  Δ                  ⁢                                                                          ⁢                                      x                    2                                                                                ⁢                      exp                          -                                                y                  2                                                  Δ                  ⁢                                                                          ⁢                                      y                    2                                                                                                          (        5        )            
FIG. 9 illustrates an allocation method of a light source and a light detector used for generation of a topographic image. 9-1 designates the location of light illumination to a light source represented by a laser or a light emitting diode. 9-2 designates the location of a light detector such as an avalanche photodiode or a photomultiplier. As illustrated in FIG. 8, since the sensitivity of ΔA at the intermediate point of the locations between the light source and light detector shows as a maximum, this intermediate point is defined as the sampling point 9-3 and as the point which gives the location information of a change in the blood volume detected with a pair of light source 9-1 and light detector 9-2. In FIG. 9, 24 sampling points are provided.
The brain activation area is established in the area enclosed by the light source, light detector, and sampling points, and a topographic image can be generated using the spatial distribution of the sensitivity given by FIG. 8 and equation (5). Therefore, a method of assessing the location accuracy will be described by referring to FIG. 10. The brain activation area 10-3 indicated as 6-4 in FIG. 6 is established for eight light illumination points 10-1 and eight light detection points 10-2 at the upper side of the skull (FIG. 10A). The central location of the brain activation area is defined as (Xc, Yc). A change in the degree of light absorption at the 24 sampling points 10-4 is obtained with the computer simulation for this brain activation area. The distribution of the changes in light absorption degree is then visualized as a topographic image 10-5 (FIG. 10B). The maximum point which is of the most interest to users of the system among the topographic image is defined as (Xmax, Ymax), and this maximum point has been obtained.
More concretely, the values of ΔA detected with the 24 pairs of light sources and light detectors existing in FIG. 10 are calculated as the value of ΔA at the 24 sampling points using equation (5) for changes in light absorption degree at the central location (Xc, Yc) of the preset brain activation (S1-1 in FIG. 21). A topographic image can be visualized (S1-2 in FIG. 21) with the spatial interpolation process using the value of each ΔA. The location information for obtaining such ΔA and the maximum location (Xmax, Ymax) thereof has been obtained (S1-3 in FIG. 21). The interpolation process has been executed in this embodiment with the method (inverse distance method) described in Non-Patent Document 5.
Displacement can be detected by comparing the central location (Xc, Yc) of the brain activation area and the maximum location (Xmax, Ymax) of the topographic image. Such displacement is generated because the interpolation is executed using ΔA at each sampling point and the location coordinate thereof in order to generate a topographic image with the spatial interpolation. The topographic image intensity reflects the location information at each sampling point.
The maximum point (Xmax, Ymax) for the central point (Xc, Yc) obtained by simulation is expressed with vectors (S1-4 in FIG. 21) and the resulting obtained distribution is illustrated in FIG. 11. Moreover, the flowchart for obtaining distribution of the displacement information is illustrated in FIG. 21.
FIG. 11A shows light illumination points 11-1; light detection points 11-2; and sampling points 11-3. In this figure, the point (Xc, Yc) is ranged as −15≦(Xc, Yc)≦15 and the point (Xmax, Ymax) for the each point (Xc, Yc) within this range has been obtained. In FIG. 11B, the starting point of each vector is (Xc, Yc) and the end point thereof corresponds to (Xmax, Ymax), showing the displacement of the topographic image. This result suggests that the displacement of the topographic image shows the following trends:
(1) When the brain activation area is visualized with a topographic image, the topographic image is displayed with displacement toward the sampling point which is nearest to the center of the brain activation area.
(2) When the topographic image is displayed with a certain displacement toward the sampling point, this topographic image is never displayed over the sampling points.
(3) At the central point (Xc1, Yc1) of a certain brain activation area and a point (Xc2, Yc2) which is farther from the sampling point than a point (Xc1, Yc1), a displacement toward the sampling point becomes larger in the point (Xc2, Yc2).
Here, the actual brain is different among different individuals in many parameters. For example, different people will have different: thicknesses of each skull, cerebrospinal fluid, and/or cortex layer illustrated in FIG. 6; scattering coefficient characteristics for the scattering of light, light absorbing coefficient characteristics for the absorption of light, sizes of activation areas, and changes in the light absorbing coefficient due to brain activation. Therefore, it is necessary to check whether the trends in displacement of the topographic image described in the items (1) to (3) are usually generated among the individuals or not. However, it is difficult to realize the simulation by changing all of the numerous parameters.
Accordingly, while the effects, which are similar to that in the simulation in which numerous parameters are changed, are obtained, a more simplified simulation can be realized by conducting the simulation through changes of the values of Δx and Δy in the elliptic function of the equation (5). The reason is that these are values depending on the parameters. As a result of simulation through changes of the values of Δx and Δy, it has been confirmed that the trends in displacement of the topographic image described in the items (1) through (3) are never changed even when the values of Δx and Δy are changed.
Namely, it has been confirmed that the trends of such displacement never change among the individuals (difference in thickness or other characteristics of the skull, cerebrospinal fluid layer, cortex) and these changes occur in general.